Overview

Dataset statistics

Number of variables15
Number of observations1094942
Missing cells8576417
Missing cells (%)52.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.3 MiB
Average record size in memory120.0 B

Variable types

Numeric11
DateTime2
Categorical2

Alerts

department_name has a high cardinality: 439 distinct values High cardinality
height has a high cardinality: 1081 distinct values High cardinality
bp_systolic is highly correlated with bp_diastolicHigh correlation
bp_diastolic is highly correlated with bp_systolicHigh correlation
bp_systolic is highly correlated with bp_diastolicHigh correlation
bp_diastolic is highly correlated with bp_systolicHigh correlation
bp_systolic is highly correlated with bp_diastolicHigh correlation
bp_diastolic is highly correlated with bp_systolicHigh correlation
admit_date has 51036 (4.7%) missing values Missing
discharge_date has 225126 (20.6%) missing values Missing
bp_systolic has 1027569 (93.8%) missing values Missing
bp_diastolic has 1027573 (93.8%) missing values Missing
temperature has 1038715 (94.9%) missing values Missing
pulse has 1033939 (94.4%) missing values Missing
weight has 1003189 (91.6%) missing values Missing
height has 1052611 (96.1%) missing values Missing
respirations has 1064041 (97.2%) missing values Missing
inches has 1052611 (96.1%) missing values Missing
primary_disease_id is highly skewed (γ1 = 30.89429801) Skewed
encounter_id is uniformly distributed Uniform
encounter_id has unique values Unique

Reproduction

Analysis started2021-11-26 18:02:07.908771
Analysis finished2021-11-26 18:03:11.420783
Duration1 minute and 3.51 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

encounter_id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1094942
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547471.5
Minimum1
Maximum1094942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:11.552430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile54748.05
Q1273736.25
median547471.5
Q3821206.75
95-th percentile1040194.95
Maximum1094942
Range1094941
Interquartile range (IQR)547470.5

Descriptive statistics

Standard deviation316082.6736
Coefficient of variation (CV)0.5773500055
Kurtosis-1.2
Mean547471.5
Median Absolute Deviation (MAD)273735.5
Skewness-2.227028639 × 10-17
Sum5.994495392 × 1011
Variance9.990825653 × 1010
MonotonicityNot monotonic
2021-11-26T13:03:11.874629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
590621
 
< 0.1%
58001
 
< 0.1%
78491
 
< 0.1%
17061
 
< 0.1%
37551
 
< 0.1%
139961
 
< 0.1%
160451
 
< 0.1%
99021
 
< 0.1%
119511
 
< 0.1%
Other values (1094932)1094932
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
10949421
< 0.1%
10949411
< 0.1%
10949401
< 0.1%
10949391
< 0.1%
10949381
< 0.1%
10949371
< 0.1%
10949361
< 0.1%
10949351
< 0.1%
10949341
< 0.1%
10949331
< 0.1%

patient_id
Real number (ℝ≥0)

Distinct36156
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49523.04568
Minimum1
Maximum99996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:12.025373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4963
Q125535
median49457
Q373399
95-th percentile95272
Maximum99996
Range99995
Interquartile range (IQR)47864

Descriptive statistics

Standard deviation28588.64598
Coefficient of variation (CV)0.5772796399
Kurtosis-1.154105701
Mean49523.04568
Median Absolute Deviation (MAD)23933
Skewness0.03181392522
Sum5.422486268 × 1010
Variance817310678.9
MonotonicityNot monotonic
2021-11-26T13:03:12.227839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
615604042
 
0.4%
364783313
 
0.3%
955932701
 
0.2%
671412322
 
0.2%
413312041
 
0.2%
271521902
 
0.2%
198981795
 
0.2%
341561622
 
0.1%
214271607
 
0.1%
720551496
 
0.1%
Other values (36146)1072101
97.9%
ValueCountFrequency (%)
13
 
< 0.1%
315
 
< 0.1%
818
< 0.1%
109
 
< 0.1%
135
 
< 0.1%
1444
< 0.1%
1720
< 0.1%
181
 
< 0.1%
2424
< 0.1%
2642
< 0.1%
ValueCountFrequency (%)
9999638
 
< 0.1%
99994143
< 0.1%
99989165
< 0.1%
999881
 
< 0.1%
999864
 
< 0.1%
9998438
 
< 0.1%
9998016
 
< 0.1%
999791
 
< 0.1%
999781
 
< 0.1%
999775
 
< 0.1%

admit_date
Date

MISSING

Distinct1029849
Distinct (%)98.7%
Missing51036
Missing (%)4.7%
Memory size8.4 MiB
Minimum1950-11-08 20:17:55.553000
Maximum2012-06-18 16:16:18.833000
2021-11-26T13:03:12.375135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:12.518754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

discharge_date
Date

MISSING

Distinct828469
Distinct (%)95.2%
Missing225126
Missing (%)20.6%
Memory size8.4 MiB
Minimum1950-11-09 18:59:55.553000
Maximum2010-08-21 22:00:51.647000
2021-11-26T13:03:12.672344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:12.878797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

department_name
Categorical

HIGH CARDINALITY

Distinct439
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
DOL-DEFAULT ORDERING L
712137 
PHN-PHONE
 
30300
STUDENT HEALTH SERVICE
 
23785
FCC FAMILY MEDICINE
 
15855
FCC INTERNAL MEDICINE
 
15225
Other values (434)
297640 

Length

Max length46
Median length22
Mean length19.68628475
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowETC
2nd rowETC
3rd rowHOSP DENTISTRY
4th rowBT4OB-OB GYN CLINIC
5th rowBT4OB-OB GYN CLINIC

Common Values

ValueCountFrequency (%)
DOL-DEFAULT ORDERING L712137
65.0%
PHN-PHONE30300
 
2.8%
STUDENT HEALTH SERVICE23785
 
2.2%
FCC FAMILY MEDICINE15855
 
1.4%
FCC INTERNAL MEDICINE15225
 
1.4%
EYE CLINIC11253
 
1.0%
ETC9420
 
0.9%
CLINICAL CANCER CENTER9075
 
0.8%
PSY ADULT PSYCHIATRY CLINIC8970
 
0.8%
NOT ASSIGNED8916
 
0.8%
Other values (429)250006
 
22.8%

Length

2021-11-26T13:03:13.048344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dol-default712137
23.5%
l712137
23.5%
ordering712137
23.5%
clinic71070
 
2.3%
medicine34607
 
1.1%
health33889
 
1.1%
fcc33120
 
1.1%
phn-phone30300
 
1.0%
rad30266
 
1.0%
service23785
 
0.8%
Other values (475)640713
21.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ)

Distinct95
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean44.83028325
Minimum-3.13
Maximum90
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size8.4 MiB
2021-11-26T13:03:13.230853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.13
5-th percentile17
Q130
median45
Q358
95-th percentile77
Maximum90
Range93.13
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.55365283
Coefficient of variation (CV)0.4138642785
Kurtosis-0.6536845854
Mean44.83028325
Median Absolute Deviation (MAD)14
Skewness0.1601038
Sum49086246.19
Variance344.2380335
MonotonicityNot monotonic
2021-11-26T13:03:13.371475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5024179
 
2.2%
4724000
 
2.2%
4823932
 
2.2%
4923853
 
2.2%
5122978
 
2.1%
4622194
 
2.0%
5221734
 
2.0%
3121586
 
2.0%
3021552
 
2.0%
4421247
 
1.9%
Other values (85)867680
79.2%
ValueCountFrequency (%)
-3.131
 
< 0.1%
-1.721
 
< 0.1%
0.011
 
< 0.1%
0.421
 
< 0.1%
0.611
 
< 0.1%
11
 
< 0.1%
26
 
< 0.1%
3192
 
< 0.1%
41502
0.1%
53004
0.3%
ValueCountFrequency (%)
903766
0.3%
891171
 
0.1%
881738
 
0.2%
871811
 
0.2%
862538
0.2%
853324
0.3%
843613
0.3%
834076
0.4%
824424
0.4%
815115
0.5%

bp_systolic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct191
Distinct (%)0.3%
Missing1027569
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean128.3093524
Minimum10
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:13.498852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile100
Q1116
median127
Q3140
95-th percentile159
Maximum250
Range240
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.45413998
Coefficient of variation (CV)0.1438253692
Kurtosis1.630997318
Mean128.3093524
Median Absolute Deviation (MAD)12
Skewness0.3004511431
Sum8644586
Variance340.5552823
MonotonicityNot monotonic
2021-11-26T13:03:13.639987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1202738
 
0.3%
1102487
 
0.2%
1302054
 
0.2%
1222023
 
0.2%
1241806
 
0.2%
1281795
 
0.2%
1321688
 
0.2%
1181674
 
0.2%
1121545
 
0.1%
1261507
 
0.1%
Other values (181)48056
 
4.4%
(Missing)1027569
93.8%
ValueCountFrequency (%)
104
 
< 0.1%
1114
< 0.1%
126
< 0.1%
141
 
< 0.1%
153
 
< 0.1%
167
< 0.1%
171
 
< 0.1%
184
 
< 0.1%
241
 
< 0.1%
262
 
< 0.1%
ValueCountFrequency (%)
2501
< 0.1%
2481
< 0.1%
2452
< 0.1%
2402
< 0.1%
2341
< 0.1%
2301
< 0.1%
2291
< 0.1%
2271
< 0.1%
2251
< 0.1%
2241
< 0.1%

bp_diastolic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct122
Distinct (%)0.2%
Missing1027573
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean74.53116419
Minimum0
Maximum173
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:13.833470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q167
median74
Q381
95-th percentile93
Maximum173
Range173
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.86601368
Coefficient of variation (CV)0.1457915464
Kurtosis0.9619839874
Mean74.53116419
Median Absolute Deviation (MAD)7
Skewness0.3035477067
Sum5021090
Variance118.0702532
MonotonicityNot monotonic
2021-11-26T13:03:13.999027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
703725
 
0.3%
803628
 
0.3%
722826
 
0.3%
692793
 
0.3%
742677
 
0.2%
782670
 
0.2%
682596
 
0.2%
762546
 
0.2%
672477
 
0.2%
822409
 
0.2%
Other values (112)39022
 
3.6%
(Missing)1027573
93.8%
ValueCountFrequency (%)
01
< 0.1%
71
< 0.1%
91
< 0.1%
122
< 0.1%
141
< 0.1%
161
< 0.1%
182
< 0.1%
191
< 0.1%
241
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1731
< 0.1%
1641
< 0.1%
1461
< 0.1%
1431
< 0.1%
1421
< 0.1%
1411
< 0.1%
1391
< 0.1%
1381
< 0.1%
1342
< 0.1%
1331
< 0.1%

temperature
Real number (ℝ≥0)

MISSING

Distinct156
Distinct (%)0.3%
Missing1038715
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean97.43807192
Minimum78.08
Maximum106.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:14.152626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum78.08
5-th percentile95.7
Q196.8
median97.5
Q398.1
95-th percentile99.14
Maximum106.16
Range28.08
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.087830855
Coefficient of variation (CV)0.01116433068
Kurtosis6.156666997
Mean97.43807192
Median Absolute Deviation (MAD)0.7
Skewness-0.3700511841
Sum5478650.47
Variance1.18337597
MonotonicityNot monotonic
2021-11-26T13:03:14.368225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.74105
 
0.4%
96.84001
 
0.4%
97.522886
 
0.3%
97.162885
 
0.3%
97.882762
 
0.3%
97.342701
 
0.2%
98.062553
 
0.2%
96.982474
 
0.2%
98.242355
 
0.2%
98.62245
 
0.2%
Other values (146)27260
 
2.5%
(Missing)1038715
94.9%
ValueCountFrequency (%)
78.081
< 0.1%
78.621
< 0.1%
79.521
< 0.1%
86.722
< 0.1%
87.981
< 0.1%
90.321
< 0.1%
90.51
< 0.1%
91.41
< 0.1%
91.61
< 0.1%
91.761
< 0.1%
ValueCountFrequency (%)
106.161
< 0.1%
105.981
< 0.1%
104.721
< 0.1%
104.541
< 0.1%
103.641
< 0.1%
103.51
< 0.1%
103.461
< 0.1%
103.31
< 0.1%
103.282
< 0.1%
103.12
< 0.1%

pulse
Real number (ℝ≥0)

MISSING

Distinct144
Distinct (%)0.2%
Missing1033939
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean78.16727046
Minimum0
Maximum174
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:14.658491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q168
median77
Q387
95-th percentile104
Maximum174
Range174
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.56003354
Coefficient of variation (CV)0.186267647
Kurtosis1.046809655
Mean78.16727046
Median Absolute Deviation (MAD)9
Skewness0.5111758361
Sum4768438
Variance211.9945768
MonotonicityNot monotonic
2021-11-26T13:03:14.833739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
803579
 
0.3%
723122
 
0.3%
763006
 
0.3%
682330
 
0.2%
842020
 
0.2%
881938
 
0.2%
741910
 
0.2%
701831
 
0.2%
641729
 
0.2%
861577
 
0.1%
Other values (134)37961
 
3.5%
(Missing)1033939
94.4%
ValueCountFrequency (%)
01
 
< 0.1%
11
 
< 0.1%
23
< 0.1%
42
< 0.1%
62
< 0.1%
71
 
< 0.1%
84
< 0.1%
102
< 0.1%
112
< 0.1%
122
< 0.1%
ValueCountFrequency (%)
1742
< 0.1%
1731
 
< 0.1%
1701
 
< 0.1%
1681
 
< 0.1%
1631
 
< 0.1%
1602
< 0.1%
1581
 
< 0.1%
1571
 
< 0.1%
1523
< 0.1%
1511
 
< 0.1%

weight
Real number (ℝ≥0)

MISSING

Distinct4535
Distinct (%)4.9%
Missing1003189
Missing (%)91.6%
Infinite0
Infinite (%)0.0%
Mean2967.021848
Minimum1.76
Maximum12345.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:15.007307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.76
5-th percentile1872.96
Q12361.6
median2839.52
Q33428.48
95-th percentile4514.88
Maximum12345.76
Range12344
Interquartile range (IQR)1066.88

Descriptive statistics

Standard deviation845.2281299
Coefficient of variation (CV)0.2848742521
Kurtosis3.101110209
Mean2967.021848
Median Absolute Deviation (MAD)522.08
Skewness1.115686512
Sum272233155.6
Variance714410.5916
MonotonicityNot monotonic
2021-11-26T13:03:15.163309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3033.44208
 
< 0.1%
2257.44204
 
< 0.1%
2292.64203
 
< 0.1%
2786.56191
 
< 0.1%
3104191
 
< 0.1%
2821.76190
 
< 0.1%
2363.2190
 
< 0.1%
3068.8188
 
< 0.1%
2610.24185
 
< 0.1%
2680.64183
 
< 0.1%
Other values (4525)89820
 
8.2%
(Missing)1003189
91.6%
ValueCountFrequency (%)
1.763
< 0.1%
1.921
 
< 0.1%
2.083
< 0.1%
2.242
< 0.1%
2.43
< 0.1%
2.561
 
< 0.1%
2.723
< 0.1%
2.882
< 0.1%
3.042
< 0.1%
3.21
 
< 0.1%
ValueCountFrequency (%)
12345.762
< 0.1%
11746.081
< 0.1%
11534.41
< 0.1%
10751.391
< 0.1%
10638.41
< 0.1%
10543.21
< 0.1%
10197.441
< 0.1%
10179.841
< 0.1%
10172.81
< 0.1%
10123.521
< 0.1%

height
Categorical

HIGH CARDINALITY
MISSING

Distinct1081
Distinct (%)2.6%
Missing1052611
Missing (%)96.1%
Memory size8.4 MiB
5' 2.99"
 
1245
5' 6.92"
 
1107
5' 4.96"
 
1098
5' 6.14"
 
884
5' 8.11"
 
804
Other values (1076)
37193 

Length

Max length10
Median length8
Mean length7.519288465
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)1.0%

Sample

1st row5' 5.354"
2nd row5' 5.157"
3rd row5' 5.5"
4th row5' 5"
5th row5' 5.5"

Common Values

ValueCountFrequency (%)
5' 2.99"1245
 
0.1%
5' 6.92"1107
 
0.1%
5' 4.96"1098
 
0.1%
5' 6.14"884
 
0.1%
5' 8.11"804
 
0.1%
5' 5.74"792
 
0.1%
5' 5"779
 
0.1%
5' 7"768
 
0.1%
5' 4.17"762
 
0.1%
5' 7.71"759
 
0.1%
Other values (1071)33333
 
3.0%
(Missing)1052611
96.1%

Length

2021-11-26T13:03:15.311913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
536873
43.6%
65242
 
6.2%
42113
 
2.5%
2.991245
 
1.5%
21202
 
1.4%
6.921107
 
1.3%
4.961099
 
1.3%
6.14887
 
1.0%
0869
 
1.0%
8.11804
 
0.9%
Other values (874)33223
39.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

respirations
Real number (ℝ≥0)

MISSING

Distinct74
Distinct (%)0.2%
Missing1064041
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean16.95291415
Minimum1
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:15.445553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q116
median16
Q318
95-th percentile20
Maximum186
Range185
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.382240158
Coefficient of variation (CV)0.2584948004
Kurtosis492.3767844
Mean16.95291415
Median Absolute Deviation (MAD)1
Skewness17.20909483
Sum523862
Variance19.20402881
MonotonicityNot monotonic
2021-11-26T13:03:15.619091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1615422
 
1.4%
185440
 
0.5%
204098
 
0.4%
142344
 
0.2%
121712
 
0.2%
24520
 
< 0.1%
22381
 
< 0.1%
17254
 
< 0.1%
15214
 
< 0.1%
2881
 
< 0.1%
Other values (64)435
 
< 0.1%
(Missing)1064041
97.2%
ValueCountFrequency (%)
11
 
< 0.1%
21
 
< 0.1%
61
 
< 0.1%
84
 
< 0.1%
91
 
< 0.1%
1036
 
< 0.1%
119
 
< 0.1%
121712
0.2%
1356
 
< 0.1%
142344
0.2%
ValueCountFrequency (%)
1861
< 0.1%
1731
< 0.1%
1692
< 0.1%
1661
< 0.1%
1651
< 0.1%
1611
< 0.1%
1521
< 0.1%
1371
< 0.1%
1341
< 0.1%
1292
< 0.1%

primary_disease_id
Real number (ℝ)

SKEWED

Distinct1175
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.4308703
Minimum-1
Maximum238763
Zeros0
Zeros (%)0.0%
Negative1092391
Negative (%)99.8%
Memory size8.4 MiB
2021-11-26T13:03:15.830569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum238763
Range238764
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4985.1677
Coefficient of variation (CV)26.59736729
Kurtosis1035.713013
Mean187.4308703
Median Absolute Deviation (MAD)0
Skewness30.89429801
Sum205225932
Variance24851896.99
MonotonicityNot monotonic
2021-11-26T13:03:16.025045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11092391
99.8%
9325671
 
< 0.1%
1490264
 
< 0.1%
14189658
 
< 0.1%
7102339
 
< 0.1%
1541827
 
< 0.1%
4346721
 
< 0.1%
9430819
 
< 0.1%
15340419
 
< 0.1%
10310417
 
< 0.1%
Other values (1165)2216
 
0.2%
ValueCountFrequency (%)
-11092391
99.8%
9206
 
< 0.1%
9701
 
< 0.1%
12831
 
< 0.1%
14283
 
< 0.1%
14672
 
< 0.1%
15222
 
< 0.1%
15681
 
< 0.1%
15702
 
< 0.1%
16052
 
< 0.1%
ValueCountFrequency (%)
2387631
< 0.1%
2387621
< 0.1%
2386391
< 0.1%
2383671
< 0.1%
2381801
< 0.1%
2377941
< 0.1%
2377691
< 0.1%
2377101
< 0.1%
2375511
< 0.1%
2333531
< 0.1%

inches
Real number (ℝ≥0)

MISSING

Distinct1061
Distinct (%)2.5%
Missing1052611
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean66.10251465
Minimum0.19
Maximum407.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2021-11-26T13:03:16.215540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile60.23
Q163.386
median66.14
Q369
95-th percentile73
Maximum407.95
Range407.76
Interquartile range (IQR)5.614

Descriptive statistics

Standard deviation6.254104452
Coefficient of variation (CV)0.09461220175
Kurtosis353.925432
Mean66.10251465
Median Absolute Deviation (MAD)2.76
Skewness4.580385401
Sum2798185.548
Variance39.11382249
MonotonicityNot monotonic
2021-11-26T13:03:16.411591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.991245
 
0.1%
66.921107
 
0.1%
64.961098
 
0.1%
66.14884
 
0.1%
68.11804
 
0.1%
65.74792
 
0.1%
65779
 
0.1%
67768
 
0.1%
64.17762
 
0.1%
67.71759
 
0.1%
Other values (1051)33333
 
3.0%
(Missing)1052611
96.1%
ValueCountFrequency (%)
0.191
 
< 0.1%
0.571
 
< 0.1%
1.182
 
< 0.1%
1.966
< 0.1%
22
 
< 0.1%
2.011
 
< 0.1%
2.043
< 0.1%
2.081
 
< 0.1%
2.165
< 0.1%
2.21
 
< 0.1%
ValueCountFrequency (%)
407.951
< 0.1%
3221
< 0.1%
246.221
< 0.1%
242.121
< 0.1%
2211
< 0.1%
210.781
< 0.1%
194.251
< 0.1%
181.311
< 0.1%
108.381
< 0.1%
108.261
< 0.1%

Interactions

2021-11-26T13:03:00.231665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:35.524300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:38.722316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:42.184059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:45.231399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.192427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:49.120426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:51.019244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:52.924792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:54.807171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:56.881019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:00.387754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:36.057876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:39.221981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:42.709656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:45.392011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.349038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:49.313940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:51.194808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:53.096328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:54.973764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:57.404618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:00.578247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:36.588455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:39.734609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:43.302072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:45.569160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.502627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:49.473480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:51.366316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:53.288781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:55.158872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:57.921251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:00.737845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:36.746184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:39.916125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:43.459248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:45.773616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.662085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:49.673290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:51.531606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:53.460376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:55.319514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:58.098796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:00.873488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:36.905729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:40.115594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:43.637776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:45.980084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.805619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:49.863024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:51.703119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:53.614923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:55.483070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:58.274299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:01.023056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:37.071286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:40.325082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:43.800302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:46.179556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.966051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:50.029580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:51.896635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:53.773525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:55.639649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:58.791967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:01.168669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:37.228893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:40.491586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:43.965859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:46.354121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:48.121117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:50.219073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:52.057200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:53.935536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:55.787225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:59.063183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:01.346192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:37.401403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:40.716984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:44.129450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:46.500764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:48.366474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:50.378679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:52.257675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:54.103086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:55.937574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:59.312517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:01.491327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:37.554853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:40.868577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:44.299890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:46.652165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:48.558925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:50.528283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:52.430208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:54.258677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:56.087181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:59.465675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:01.707748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:38.064076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:41.422098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:44.898292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:46.855350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:48.752429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:50.688847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:52.590501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:54.448170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:56.247746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:59.916512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:01.865327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:38.223683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:41.581706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:45.050882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:47.018896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:48.925974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:50.841758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:52.746051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:54.615694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:02:56.390358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-26T13:03:00.075077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-26T13:03:16.550221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-26T13:03:16.768669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-26T13:03:16.950150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-26T13:03:17.120694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-26T13:03:02.344053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-26T13:03:04.032536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-26T13:03:08.833702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-26T13:03:09.792137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

encounter_idpatient_idadmit_datedischarge_datedepartment_nameagebp_systolicbp_diastolictemperaturepulseweightheightrespirationsprimary_disease_idinches
0571715502102006-10-24 09:19:12.4202006-10-24 14:30:12.420ETC85.0NaNNaNNaNNaNNaNNoneNaN-1NaN
18802226562005-06-04 03:43:11.0532005-06-04 07:39:11.053ETC24.0NaNNaNNaNNaNNaNNoneNaN-1NaN
29923108222008-04-08 17:41:27.2832008-04-09 01:55:27.283HOSP DENTISTRY32.0NaNNaNNaNNaNNaNNoneNaN-1NaN
34401519222005-07-11 11:13:16.6072005-07-11 23:42:16.607BT4OB-OB GYN CLINIC33.0NaNNaNNaNNaNNaNNoneNaN-1NaN
492544210222008-01-23 12:43:02.770NaTBT4OB-OB GYN CLINIC27.0NaNNaNNaNNaNNaNNoneNaN-1NaN
576118417202002-10-04 01:32:11.6772002-10-05 14:09:11.6777JCE18.0NaNNaNNaNNaNNaNNoneNaN-1NaN
6101774819092008-08-24 03:48:43.9472008-08-24 16:56:43.947RAD GEN30.0NaNNaNNaNNaNNaNNoneNaN-1NaN
781943522752003-05-27 16:03:41.1902003-05-27 17:50:41.190ETC19.0NaNNaNNaNNaNNaNNoneNaN-1NaN
810846142463NaTNaTMAIN OR25.0NaNNaNNaNNaNNaNNoneNaN-1NaN
95624932622007-05-24 08:50:36.040NaTDOL-DEFAULT ORDERING L22.0NaNNaNNaNNaNNaNNoneNaN-1NaN

Last rows

encounter_idpatient_idadmit_datedischarge_datedepartment_nameagebp_systolicbp_diastolictemperaturepulseweightheightrespirationsprimary_disease_idinches
1094932805227999961986-01-26 14:00:23.0101986-01-26 14:00:23.010DOL-DEFAULT ORDERING L34.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094933805256999961985-09-22 16:22:23.0101985-09-22 16:22:23.010DOL-DEFAULT ORDERING L34.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094934805237999961985-12-01 11:21:23.0101985-12-01 11:21:23.010DOL-DEFAULT ORDERING L34.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094935805163999961989-11-19 19:00:23.0101989-11-19 19:00:23.010DOL-DEFAULT ORDERING L38.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094936805157999961984-10-26 18:59:23.0101984-10-26 18:59:23.010DOL-DEFAULT ORDERING L33.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094937805209999961985-03-03 18:56:23.0101985-03-03 18:56:23.010DOL-DEFAULT ORDERING L33.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094938805246999961985-10-13 20:15:23.0101985-10-13 20:15:23.010DOL-DEFAULT ORDERING L34.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094939805197999961985-06-23 16:47:23.0101985-06-23 16:47:23.010DOL-DEFAULT ORDERING L33.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094940805184999961987-02-27 19:02:23.0101987-02-27 19:02:23.010DOL-DEFAULT ORDERING L35.0NaNNaNNaNNaNNaNNoneNaN-1NaN
1094941805187999961985-09-13 19:06:23.0101985-09-13 19:06:23.010DOL-DEFAULT ORDERING L34.0NaNNaNNaNNaNNaNNoneNaN-1NaN